How to tune GaussianNB?

Question

Trying to fit data with GaussianNB() gives me low accuracy score.

I'd like to try Grid Search, but it seems that parameters sigma and theta cannot be set. Is there anyway to tune GausssianNB?

Answer 1

You can tune 'var_smoothing' parameter like this:

nb_classifier = GaussianNB()

params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}
gs_NB = GridSearchCV(estimator=nb_classifier, 
                 param_grid=params_NB, 
                 cv=cv_method,   # use any cross validation technique 
                 verbose=1, 
                 scoring='accuracy') 
gs_NB.fit(x_train, y_train)

gs_NB.best_params_

Answer 2

As of version 0.20

GaussianNB().get_params().keys() returns 'priors' and 'var_smoothing'

A grid search would look like:

pipeline = Pipeline([
    ('clf', GaussianNB())
])

parameters = {
    'clf__priors': [None],
    'clf__var_smoothing': [0.00000001, 0.000000001, 0.00000001]
}

cv = GridSearchCV(pipeline, param_grid=parameters)

cv.fit(X_train, y_train)
y_pred_gnb = cv.predict(X_test)